Biblio
With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.
In order to be more environmentally friendly, a lot of parts and aspects of life become electrified to reduce the usage of fossil fuels. This can be seen in the increased number of electrical vehicles in everyday life. This of course only makes a positive impact on the environment, if the electricity is produced environmentally friendly and comes from renewable sources. But when the green electrical power is produced, it still needs to be transported to where it's needed, which is not necessarily near the production site. In China, one of the ways to do this transport is to use High Voltage Direct Current (HVDC) technology. This of course means, that the current has to be converted to DC before being transported to the end user. That implies that the converter stations are of great importance for the grid security. Therefore, a precise monitoring of the stations is necessary. Ideally, this could be accomplished with wireless sensor nodes with an autarkic energy supply. A role in this energy supply could be played by a thermoelectrical generator (TEG). But to assess the power generated in the specific environment, a simulation would be highly desirable, to evaluate the power gained from the temperature difference in the converter station. This paper proposes a method to simulate the generated power by combining a model for the generator with a Computational Fluid Dynamics (CFD) model converter.
Power network is important part of national comprehensive energy resources transmission system in the way of energy security promise and the economy society running. Meanwhile, because of many industries involved, the development of grid can push national innovation ability. Nowadays, it makes the inner of smart grid flourish that material science, computer technique and information and communication technology go forward. This paper researches the function and modality of smart grid on energy, geography and technology dimensions. The analysis on the technology dimension is addressed on two aspects which are network control and interaction with customer. The mapping relationship between functions fo smart grid and eight key technologies, which are Large-capacity flexible transmission technology, DC power distribution technology, Distributed power generation technology, Large-scale energy storage technology, Real-time tracking simulation technology, Intelligent electricity application technology, The big data analysis and cloud computing technology, Wide-area situational awareness technology, is given. The research emphasis of the key technologies is proposed.
Advanced Metering Infrastructure (AMI) is the core component in a smart grid that exhibits a highly complex network configuration. AMI shares information about consumption, outages, and electricity rates reliably and efficiently by bidirectional communication between smart meters and utilities. However, the numerous smart meters being connected through mesh networks open new opportunities for attackers to interfere with communications and compromise utilities assets or steal customers private information. In this paper, we present a new DoS attack, called puppet attack, which can result in denial of service in AMI network. The intruder can select any normal node as a puppet node and send attack packets to this puppet node. When the puppet node receives these attack packets, this node will be controlled by the attacker and flood more packets so as to exhaust the network communication bandwidth and node energy. Simulation results show that puppet attack is a serious and packet deliver rate goes down to 20%-10%.
We consider the problem of designing (or augmenting) an electric power system at a minimum cost such that it satisfies the N-k-ε survivability criterion. This survivability criterion is a generalization of the well-known N-k criterion, and it requires that at least (1-εj) fraction of the steady-state demand be met after failures of j components, for j=0,1,...,k. The network design problem adds another level of complexity to the notoriously hard contingency analysis problem, since the contingency analysis is only one of the requirements for the design optimization problem. We present a mixed-integer programming formulation of this problem that takes into account both transmission and generation expansion. We propose an algorithm that can avoid combinatorial explosion in the number of contingencies, by seeking vulnerabilities in intermediary solutions and constraining the design space accordingly. Our approach is built on our ability to identify such system vulnerabilities quickly. Our empirical studies on modified instances of the IEEE 30-bus and IEEE 57-bus systems show the effectiveness of our methods. We were able to solve the transmission and generation expansion problems for k=4 in approximately 30 min, while other approaches failed to provide a solution at the end of 2 h.
The Department of Energy seeks to modernize the U.S. electric grid through the SmartGrid initiative, which includes the use of Global Positioning System (GPS)-timing dependent electric phasor measurement units (PMUs) for continual monitoring and automated controls. The U.S. Department of Homeland Security is concerned with the associated risks of increased utilization of GPS timing in the electricity subsector, which could in turn affect a large number of electricity-dependent Critical Infrastructure (CI) sectors. Exploiting the vulnerabilities of GPS systems in the electricity subsector can result to large-scale and costly blackouts. This paper seeks to analyze the risks of increased dependence of GPS into the electric grid through the introduction of PMUs and provides a systems engineering perspective to the GPS-dependent System of Systems (S-o-S) created by the SmartGrid initiative. The team started by defining and modeling the S-o-S followed by usage of a risk analysis methodology to identify and measure risks and evaluate solutions to mitigating the effects of the risks. The team expects that the designs and models resulting from the study will prove useful in terms of determining both current and future risks to GPS-dependent CIs sectors along with the appropriate countermeasures as the United States moves towards a SmartGrid system.
More and more intelligent functions are proposed, designed and implemented in meters to make the power supply be smart. However, these complex functions also bring risks to the smart meters, and they become susceptible to vulnerabilities and attacks. We present the rat-group attack in this paper, which exploits the vulnerabilities of smart meters in the cyber world, but spreads in the physical world due to the direct economic benefits. To the best of our knowledge, no systematic work has been conducted on this attack. Game theory is then applied to analyze this attack, and two game models are proposed and compared under different assumptions. The analysis results suggest that the power company shall follow an open defense policy: disclosing the defense parameters to all users (i.e., the potential attackers), results in less loss in the attack.
Vehicle-to-grid (V2G), involving both charging and discharging of battery vehicles (BVs), enhances the smart grid substantially to alleviate peaks in power consumption. In a V2G scenario, the communications between BVs and power grid may confront severe cyber security vulnerabilities. Traditionally, authentication mechanisms are solely designed for the BVs when they charge electricity as energy customers. In this paper, we first show that, when a BV interacts with the power grid, it may act in one of three roles: 1) energy demand (i.e., a customer); 2) energy storage; and 3) energy supply (i.e., a generator). In each role, we further demonstrate that the BV has dissimilar security and privacy concerns. Hence, the traditional approach that only considers BVs as energy customers is not universally applicable for the interactions in the smart grid. To address this new security challenge, we propose a role-dependent privacy preservation scheme (ROPS) to achieve secure interactions between a BV and power grid. In the ROPS, a set of interlinked subprotocols is proposed to incorporate different privacy considerations when a BV acts as a customer, storage, or a generator. We also outline both centralized and distributed discharging operations when a BV feeds energy back into the grid. Finally, security analysis is performed to indicate that the proposed ROPS owns required security and privacy properties and can be a highly potential security solution for V2G networks in the smart grid. The identified security challenge as well as the proposed ROPS scheme indicates that role-awareness is crucial for secure V2G networks.